Loading in 2 Seconds...
Loading in 2 Seconds...
Silas Little Experimental Forest New Jersey. Climate, Fire, and Carbon Cycle Sciences Research Work Unit NRS-06. Rich Birdsey (project leader, Newtown Square, PA) Warren Heilman (Group Leader) Jay Charney (Research Meteorologist Xindi Bian (Meteorologist).
Climate, Fire, and Carbon Cycle Sciences
Research Work Unit NRS-06
Rich Birdsey (project leader, Newtown Square, PA)
Warren Heilman (Group Leader)
Jay Charney (Research Meteorologist
Xindi Bian (Meteorologist)
Potential Future Changes in the Atmospheric Component of Fire Risk.
Haines Index Climatology using the North American Regional Reanalysis
A modular modeling system that enables fire information, consumption, and smoke modeling.
Lesley Fusina, Sharon Zhong. Jay Charney and Xindi Bian
Evaluate model predictions of smoke using observational data from the October 2007 Southern California Wildfire outbreaks. The results indicate that while smoke models can currently predict smoke trajectories, the timing of smoke impacts and predictions of ground concentrations need improvement.
Warren E. Heilman, Research Meteorologist, US Forest Service Northern Research Station
David Hollinger, Plant Physiologist, US Forest Service Northern Research Station
Ken Clark, Research Forester, US Forest Service Northern Research Station
Xindi Bian, US Forest Service Northern Research Station
Richard Birdsey, Project Leader, US Forest Service Northern Research Station
Yude Pan, Research Forester, US Forest Service Northern Research Station
Sharon Zhong, Atmospheric Scientist, Michigan State University
Xiuping Li, Visiting Scientist, Michigan State University
Smoke Dispersion from Low-Intensity Fires
Principal Investigator: Warren E. Heilman
OSU RAFLES Model
NJ Pine Barrens
Pine overstory, Vaccinium with Oak understory 265 Acres
1 km NW of burn site
Plow lines (Ignitions along lines from south to north)
30 m Tower
10 m Tower
20 m Tower
3 m Tower
Investigating the differences between the gridded meteorological fields produced by the Real Time Mesoscale Analysis (RTMA) and observed meteorological conditions at Remote Automated Weather Stations (RAWS) in the northeastern United States.
The differences will be analyzed and tools developed to improve the interpretation of NWS fire weather forecasts and clarify their role in fire management decision making.